Welcome to the world of collaborative perception! In this article, we will explore how to set up and utilize the HEAL framework for LiDAR-based and camera-based data in heterogeneous settings. We’ll walk you through the installation process, data preparation, basic training commands, and troubleshooting tips to help you get started on your collaborative perception journey.

Understanding the HEAL Framework

HEAL stands for HEterogeneous ALliance. Think of it as a collaborative orchestra where each instrument (or agent) can play its unique melody using different types of sensors (LiDAR and cameras) to create a harmonious understanding of the environment. By seamlessly integrating various modalities, HEAL offers a comprehensive tool for multi-agent collaborative perception.

Installation Steps

Here’s a step-by-step guide to installing HEAL on your system:

Step 1: Basic Installation

• Open your terminal and create a new conda environment:

bash
conda create -n heal python=3.8
conda activate heal

• Install PyTorch and dependencies:

bash
conda create -n coalign python=3.8 pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.6 -c pytorch -c conda-forge
pip install -r requirements.txt
python setup.py develop

Step 2: Install Spconv

Spconv is essential for generating voxel features. Depending on your usage:

• For **spconv 2.x**, run:

bash
pip install spconv-cu116  # Change according to your CUDA version.

• For **spconv 1.2.1**, follow the setup guide from here.

Step 3: Prepare for Training

Create a folder for logs and prepare the agent types for your experiments:

bash
mkdir opencoodlogs

Data Preparation

HEAL supports various datasets like OPV2V, V2XSet, and DAIR-V2X-C. Here’s how to prepare your data:

• Download your preferred datasets and place them in a folder named ‘HEAL’. Make sure to maintain a consistent structure as specified in the README.
• For example, place your data in:

HEAL/dataset/my_dair_v2x/v2x_c/v2x_i/v2x_v/
HEAL/dataset/OPV2V/additional/test/train/validate/

Training the Model

To train your model, you’ll need to use a configuration file. Here’s how:

• Run the following command to start training:

bash
python opencood/tools/train.py -y $CONFIG_FILE [--model_dir $CHECKPOINT_FOLDER]

• You can also use distributed data parallel (DDP) if you have multiple GPUs:

bash
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 --use_env opencood/tools/train_ddp.py -y $CONFIG_FILE [--model_dir $CHECKPOINT_FOLDER]

Troubleshooting Tips

If you encounter issues during installation or execution, consider the following troubleshooting tips:

• Ensure that all dependencies are correctly installed by checking the installation logs.
• If the model fails to train or gives an unexpected error, verify the structure of your dataset folder matches the guidelines provided.
• If you experience issues with spconv compatibility, switch between versions as required and check your CUDA installation.

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Conclusion

With HEAL, you are well-equipped to dive into the intriguing field of multi-agent collaborative perception. Utilize this extensible framework to harness the power of heterogeneous data and contribute to the advancement of AI.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.